Last updated: 2019-01-04
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| Name | Class | Size |
|---|---|---|
| alignMerge | cwlStepParam | 301.6 Kb |
| fq1 | list | 832 bytes |
| fq2 | list | 832 bytes |
| inputList | list | 3.5 Kb |
| mc3 | cwlStepParam | 90.6 Kb |
| paramList | list | 576 bytes |
| rgs | list | 880 bytes |
| samples | list | 592 bytes |
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The Multi-Center Mutation Calling in Multiple Cancers project (MC3) pipeline was developed by TCGA to generate a comprehensive encyclopedia of somatic mutation calls by applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. More details can be found in this paper: Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines
The mc3 pipeline is available at https://github.com/OpenGenomics/mc3. All required software have been deployed in cloud with docker.
The pipeline has been imported and contained in the RcwlPipelines pacakge, which contains two major steps (markID step was removed):
Here is the short summary.
suppressPackageStartupMessages(library(Rcwl))
library(RcwlPipelines)
data(mc3)
short(mc3)
inputs:
- tumorID
- normalID
- tumor
- normal
- bed_file
- centromere
- cosmic
- dbsnp
- refFasta
- vepData
outputs:
- outmaf
- outvcf
steps:
- call_variants
- convert
plotCWL(mc3)
callVar <- readCWL(runs(mc3)$call_variants)
plotCWL(callVar)
conv <- readCWL(runs(mc3)$convert)
plotCWL(conv)
Testing somatic mutation data can be download from: https://github.com/genome/somatic-snv-test-data.
inputList. The tumorID/normalID must be consistent with SM from BAM read group.inputList <- list(tumorID=list(test="NA12892"),
normalID=list(test="NA12878"),
tumor=list(test="data/tumor.bam"),
normal=list(test="data/normal.bam"))
paramList.paramList <- list(bed_file="/mnt/lustre/users/qhu/software/mc3/data/gaf_20111020+broad_wex_1.1_hg19.bed",
centromere="/mnt/lustre/users/qhu/software/mc3/data/centromere_hg19.bed",
cosmic="/mnt/lustre/users/qhu/software/mc3/data/hg19_cosmic_v54_120711.vcf.gz",
dbsnp="/mnt/lustre/users/qhu/software/mc3/data/dbsnp_134_b37.leftAligned.vcf.gz",
refFasta="/rpcc/bioinformatics/reference/current/human_g1k_v37.fa.gz",
vepData="/home/qhu/.vep/")
res <- runCWLBatch(mc3, wdir = "output/mc3",
inputList = inputList, paramList = paramList,
BPPARAM = BatchtoolsParam(workers = 1, cluster = "sge",
template = "/rpcc/bioinformatics/sge_centos7.tmpl",
resources = list(threads = 2,
queue = "centos7.q")))
The final VCF was filtered and merged from the outputs of different pipelines and annotated by VEP. The converted MAF file was also generated.
dir("output/mc3/test")
[1] "merged.vep.vcf" "vep.maf"
vcf <- read.table("output/mc3/test/merged.vep.vcf", sep="\t")
head(vcf)
V1 V2 V3 V4 V5 V6 V7
1 21 10400299 . A T 0.0 PASS
2 21 10400380 . C T . PASS
3 21 10402435 rs2948877 G A . PASS
4 21 10402715 . G A 0.0 PASS
5 21 10402795 rs148043841 G T . PASS
6 21 10402985 . G GA . PASS
V8
1 CENTERS=RADIA|VARSCANS|MUSE|SOMATICSNIPER;CSQ=T|intergenic_variant|MODIFIER|||||||||||||||rs370695467|1||||1|SNV|1|||||||||||||||||||||||||||||||
2 CENTERS=SOMATICSNIPER|RADIA|VARSCANS|MUSE;CSQ=T|intergenic_variant|MODIFIER||||||||||||||||1||||1|SNV|1|||||||||||||||||||||||||||||||
3 CENTERS=MUSE|RADIA|VARSCANS|SOMATICSNIPER;CSQ=A|intergenic_variant|MODIFIER|||||||||||||||rs2948877|1||||1|SNV|1||||||||||||||||A:0.3626|A:0.4266|A:0.3228|A:0.3075|A:0.2913|A:0.4346||||||||||
4 CENTERS=RADIA|VARSCANS|MUSE;CSQ=A|intergenic_variant|MODIFIER|||||||||||||||rs2948878|1||||1|SNV|1|||||||||||||||||||||||||||||||
5 CENTERS=MUSE|RADIA|VARSCANS;CSQ=T|intergenic_variant|MODIFIER|||||||||||||||rs373568457|1||||1|SNV|1|||||||||||||||||||||||||||||||
6 CENTERS=VARSCANI*|PINDEL;CSQ=A|intergenic_variant|MODIFIER|||||||||||||||rs375209288|1||||1|insertion|1|||||||||||||||||||||||||||||||
V9 V10 V11
1 GT:DP:AD 0/0:140:140,0 0/1:92:71,20
2 GT:DP:AD 0/0:160:160,0 0/1:117:99,18
3 GT:DP:AD 0/0:167:167,0 0/1:124:97,27
4 GT:DP:AD 0/0:145:145,0 0/1:117:97,20
5 GT:DP:AD 0/0:163:161,2 0/1:127:108,19
6 GT:DP:AD 0/0:88:88,0 0/1:82:75,7
sessionInfo()
R version 3.5.2 Patched (2018-12-31 r75935)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.4 (Final)
Matrix products: default
BLAS: /home/qhu/usr/R-3.5/lib64/R/lib/libRblas.so
LAPACK: /home/qhu/usr/R-3.5/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] RcwlPipelines_0.0.0.9000 jsonlite_1.6
[3] BiocParallel_1.16.2 Rcwl_0.99.7
[5] S4Vectors_0.20.1 BiocGenerics_0.28.0
[7] yaml_2.2.0 workflowr_1.1.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 tidyr_0.8.2 visNetwork_2.0.5
[4] prettyunits_1.0.2 assertthat_0.2.0 rprojroot_1.3-2
[7] digest_0.6.18 plyr_1.8.4 R6_2.3.0
[10] backports_1.1.3 evaluate_0.12 highr_0.7
[13] ggplot2_3.1.0 pillar_1.3.1 rlang_0.3.0.1
[16] progress_1.2.0 lazyeval_0.2.1 rstudioapi_0.8
[19] data.table_1.11.8 whisker_0.3-2 R.utils_2.7.0
[22] R.oo_1.22.0 checkmate_1.8.5 rmarkdown_1.11
[25] DiagrammeR_1.0.0 downloader_0.4 readr_1.3.1
[28] stringr_1.3.1 htmlwidgets_1.3 igraph_1.2.2
[31] munsell_0.5.0 compiler_3.5.2 influenceR_0.1.0
[34] rgexf_0.15.3 xfun_0.4 pkgconfig_2.0.2
[37] htmltools_0.3.6 tidyselect_0.2.5 gridExtra_2.3
[40] tibble_1.4.2 batchtools_0.9.11 XML_3.98-1.16
[43] viridisLite_0.3.0 crayon_1.3.4 dplyr_0.7.8
[46] withr_2.1.2 R.methodsS3_1.7.1 rappdirs_0.3.1
[49] grid_3.5.2 gtable_0.2.0 git2r_0.23.0
[52] magrittr_1.5 scales_1.0.0 stringi_1.2.4
[55] debugme_1.1.0 viridis_0.5.1 bindrcpp_0.2.2
[58] brew_1.0-6 RColorBrewer_1.1-2 tools_3.5.2
[61] glue_1.3.0 purrr_0.2.5 hms_0.4.2
[64] Rook_1.1-1 colorspace_1.3-2 base64url_1.4
[67] knitr_1.21 bindr_0.1.1
This reproducible R Markdown analysis was created with workflowr 1.1.1